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A Fully Automated Pattern Classification Method of Combining Self-Organizing Map with Generalization Regression Neural Network

  • Chao-feng Li
  • Jun-ben Zhang
  • Zheng-you Wang
  • Shi-tong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

The paper presents a new automated pattern classification method. At first original data points are partitioned by unsupervised self-organizing map network (SOM). Then from the above clustering results, some labelled points nearer to each clustering center are chosen to train supervised generalization regression neural network model (GRNN). Then utilizing the decided GRNN model, we reclassify these original data points and gain new clustering results. At last from new clustering results, we choose some labelled points nearer to new clustering center to train and classify again, and so repeat until clustering center no longer changes. Experimental results for Iris data, Wine data and remote sensing data verify the validity of our method.

Keywords

Classification Result Cluster Center Cluster Result Average Accuracy Generalize Regression Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chao-feng Li
    • 1
  • Jun-ben Zhang
    • 1
  • Zheng-you Wang
    • 2
  • Shi-tong Wang
    • 1
  1. 1.School of Information TechnologySouthern Yangtze UniversityWuxiChina
  2. 2.School of Information TechnologyJiangxi Univ. of Finance & EconomicsNanchangChina

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